






"""simple docstring"""
import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()


def 							A						(				__snake_case: Optional[int] ,     __snake_case: List[str] ,     __snake_case: Union[str, Any]  )    ->		Dict:

       """simple docstring"""





       __magic_name__      				=    LxmertConfig.from_json_file(__snake_case  )
       print(F"""Building PyTorch model from configuration: {config}"""  )
       __magic_name__      				=    LxmertForPreTraining(__snake_case  )

       # Load weights from tf checkpoint
       load_tf_weights_in_lxmert(__snake_case ,     __snake_case ,     __snake_case  )

       # Save pytorch-model
       print(F"""Save PyTorch model to {pytorch_dump_path}"""  )
       torch.save(model.state_dict() ,     __snake_case  )


if __name__ == "__main__":
 snake_case : Union[str, Any]     		=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
 )
 parser.add_argument(
     """--config_file""",
     default=None,
     type=str,
     required=True,
     help="""The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.""",
 )
 parser.add_argument(
     """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 snake_case : Optional[int]     		=		parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)







"""simple docstring"""
def 							A						(				__snake_case: int = 1_0_0_0_0_0_0  )    ->		int:

       """simple docstring"""





       __magic_name__      				=    limit + 1
       __magic_name__      				=    [0] * limit
       for first_term in range(1 ,     __snake_case  ):
              for n in range(__snake_case ,     __snake_case ,     __snake_case  ):
                     __magic_name__      				=    first_term + n / first_term
                     if common_difference % 4:  # d must be divisble by 4
                            continue
                     else:
                            common_difference /= 4
                            if (
                                first_term > common_difference
                                and first_term < 4 * common_difference
                            ):  # since x,y,z are positive integers
                                   frequency[n] += 1  # so z>0 and a>d ,also 4d<a

       __magic_name__      				=    sum(1 for x in frequency[1:limit] if x == 1_0  )

       return count


if __name__ == "__main__":
 print(f"""{solution() = }""")